In digital communication systems, a problem yet to be addressed is the presence of relatively long sequences of erroneously recognized bits (bursts). These bursts, due to disturbances of transient nature that affect the communication channel degrading the signal-to-noise ratio, worsen the overall performance of digital communication systems. It may be important to locate bursts in received data streams to read them again or to decode them in a specifically designed way.
A known method of detecting whether a burst has been received includes monitoring the envelope function of the signal to be demodulated. This implies that bursts that do not significantly modify the envelope function may hardly be detected.
Hereinafter, reference is made to communication systems in which transmitted symbols are defined as a sequence of a certain number P of bits that identifies a value of a Galois Field 2P, and in which at the demodulator side, the probability mass function (PMF) of symbols, i.e. the probability P(u=φ|Y1N) that a generic symbol u is equal to a certain value φ of the Galois field (GF) conditioned by a certain vector of samples YN has been received, is made available at the output of the demodulator. This calculation can be done regardless the kind of transmission used (QAM, PSK, On/Off, etc), or the channel characteristics (memory-less, ISI channel, etc.) using well known algorithms (for example, symbol based BCJR, as described in Wu Chang and J. R. Cruz, Fellow, “Optimal Channel Detection for Nonbinary Coded Partial Response Channels”, IEEE Transactions on Communications, vol. 57, no. 7, July 2009, symbol based soft output Viterbi algorithms (SOVA), etc.).
Burst noise worsen statistic figures of a received sequence of bits, thus increasing the probability of an erroneous recognition. The prior art burst detection algorithms are generally based on bit based analysis, rather than on symbol analysis, and are typically inadequate when demodulation is tailored on GF symbols. Among the relevant contributions in this field, the authors Mustafa N. Kaynak, Tolga M. Duma, and Erozan M. Kurtas of “Burst Error Identification for Turbo- and LDPC-Coded Magnetic Recording Systems,” IEEE Transactions on Magnetics, Vol. 40, no. 4, July 2004, 3087, consider several approaches to identify the burst. Some of them are based on evaluating distortion of the expected signal shape. This type of approach is defeated by bursts of an unexpected type that may disturb the signal in unpredictable and an unidentifiable way. Other approaches are based on constraints imposed by the error correction code. These approaches are generally more onerous because they generally require the introduction of a sort of syndrome checker to identify a potential burst. Finally, the authors of “Burst Error Identification for Turbo- and LDPC-Coded Magnetic Recording Systems,” identified above, suggest considering a reliability parameter of the decision based on the probability of bits as estimated by the demodulator (or detector). This is a more general approach.
In “Improving burst error tolerance of LDPC-centric coding systems in read channel,” IEEE Transactions on Magnetics, Vol. 46, no. 3, March 2010, 933-941, by N. Xie, T. Zhang and E. F. Haratsch, a so-called hybrid concatenated coding is proposed to determine the presence of at least a burst error in the symbols of a read word of symbols. This special and sophisticated coding scheme requires interaction of different codes and is relatively complex. Moreover, it is still based on bit-based operations, and therefore is inadequate for transmission based on GF symbols.